The Freight Analysis Framework (FAF), produced through a partnership between BTS and FHWA, integrates data from a variety of sources to create a comprehensive picture of freight movement among states and major metropolitan areas by all modes of transportation. Starting with data from the 2012 Commodity Flow Survey (CFS) and international trade data from the Census Bureau, FAF incorporates data from agriculture, extraction, utility, construction, service, and other sectors.
FAF version 4 (FAF4.1.1) provides estimates for tonnage, value, and ton-miles by regions of origin and destination, commodity type, and mode. Data are available for the base year of 2012, the recent years of 2013-2016, and forecasts from 2020 through 2045 in 5-year intervals.

Btu = British thermal unit.
1 U.S. total energy and U.S. industrial sector include 4.0 trillion Btu of net imports of coal coke that is not allocated to the States.
2 End-use sector data include electricity sales and associated electrical system energy losses.

The Transportation Services Index (TSI), created by the U.S. Department of Transportation (DOT), Bureau of Transportation Statistics (BTS), measures the movement of freight and passengers. The index, which is seasonally adjusted, combines available data on freight traffic, as well as passenger travel, that have been weighted to yield a monthly measure of transportation services output. Source: TSI numbers are BTS estimates. Note: Monthly data changes with each release due to the use of concurrent seasonal analysis, which results in seasonal analysis factors changing as each month's data are added.

Numbers may not add to totals due to rounding. The 2015 data are provisional estimates that are based on selected modal and economic trend data. All truck, rail, water, and pipeline movements that involve more than one mode, including exports and imports that change mode at international gateways, are included in multiple modes & mail to avoid double counting. As a consequence, rail and water totals in this table are less than those reported in other published sources.

The Airports dataset including other aviation facilities is as of July 6, 2017, and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). The Airports database is a geographic point database of aircraft landing facilities in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product.

The Amtrak Stations dataset is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). Updated database of the Federal Railroad Administration's (FRA) Amtrak Station database. This database is a geographic dataset containing Amtrak intercity railroad passenger terminals in the United States and Canada. Attribute data include services and passenger amenities provided at the station.

National Transportation Statistics presents statistics on the U.S. transportation system, including its physical components, safety record, economic performance, the human and natural environment, and national security.

Enplanements consist of all persons boarding a flight other than crew and passengers who boarded at an earlier stop. In previous years the source of the data for this table was the FAA, which provides information on Air Taxi operators. The current table uses data from the Office of Airline Information, which does not collect data on Air Taxi operators. General aviation passengers are also excluded from the data. Air carrier enplanements may not add to total enplanements because totals include enplanements for which carrier type is unknown.

Transportation’s contribution to the economy can be measured by its contribution to gross
domestic product (GDP). GDP is an economic measure of all goods and services produced
and consumed in the country. The transportation component of GDP can be measured as
either:
• the share of all expenditures (by households, private firms, and the government) on
final goods and services that are related to transportation (collectively known as the
final demand for transportation), or
• the contribution of transportation services produced (known as value added) to GDP

The average annual growth rate represents averages as follows: for 2000 - average from 1990 to 200, for 2010 - average from 2000 to 2010, for 2015 - average from 2010 to 2015, for 2040 - average from 2015 to 2040.

The cost of transportation stems from the resources it requires—labor, equipment, fuel, and infrastructure. Many resources are purchased by firms that provide transportation services, such as labor purchased by a railroad or fuel bought by a trucking company. Other resources are purchased directly by the users of transportation, such as fuel purchased by households for automobile travel. In addition, federal, state, and local governments provide most of the transportation infrastructure, such as highways.
The prices that transportation companies charge for transportation services become out-of-pocket costs to travelers and freight shippers, and influence their transportation choices. Because transportation is an input to the production of almost all goods and services, transportation price changes can influence the cost of other goods and services as well. Transportation prices themselves are affected by the prices of inputs, such as labor costs, fuel costs, and the costs of transportation parts.

Seasonally-Adjusted Data: Statisticians use the process of seasonal-adjustment to uncover trends in data. Monthly data, for instance, are influenced by the number of days and the number of weekends in a month as well as by the timing of holidays and seasonal activity. These influences make it difficult to see underlying changes in the data. Statisticians use seasonal adjustment to control for these influences. Controlling of seasonal influences allows measurement of real monthly changes; short and long term patterns of growth or decline; and turning points. Data for one month can be compared to data for any other month in the series and the data series can be ranked to find high and low points. Any observed differences are “real” differences; that is, they are differences brought about by changes in the data and not brought about by a change in the number of days or weekends in the month, the occurrence or non-occurrence of a holiday, or seasonal activity.